Overall our system is competitive, with best results for coverage (100%), second best for BLEU and SSA scores, and third best overall on exact match.

we admit that automatic metrics such as BLEU are not fully reliable to compare different systems, and results vary widely depending on the coverage of the systems and the specificity of the generation input.


The linear count expectations can be computed efficiently by the forward-backward recurrence for HMMs.

we have to design new algorithms for quadratic count expectations which will be done in the rest of this section.


The linguistically syntax-based models (Liu et al., 2006;Huang et al., 2006) can distinguish syntactic structures by parsing source sentence.

as an LHS tree may correspond to different RHS strings in different rules (the right two rules of Figure 1), these models also face the rule selection problem during decoding.


The MaxEnt RS model combines rich context information of grammar rules, as well as information of the subphrases which will be reduced to nonterminal X during decoding.

these information is ignored by Chiang's hierarchical model.


Here, the baseline translates the Chinese phrase " " into "booked" by using the rule: The meaning is not fully expressed since the Chinese word " " is not translated.

the MaxEnt RS model obtains a correct translation by using the rule: we also find that some results produced by the MaxEnt RS models seem to decrease the BLEU score.


As Chinese text is written without word boundaries, effectively recognizing Chinese words is like recognizing collocations in English, substituting characters for words and words for collocations.

existing topical models that involve collocations have a common limitation.


Better translation quality can be expected from pattern-based MT and example-based MT where the syntactic structure and semantics are handled together.

pattern-based MT require immense pattern dictionaries that are difficult to develop(Jung et al., 1999;Uchino et al., 2001).


The results show that the whole dictionary covered almost all input sentences.

many cases of matches to semantically inappropriate patterns occurred, and the semantic coverage decreased to 78% when these were eliminated.


This result also shows that the accuracies of the SR algorithm and CC algorithm are comparable when using the same features.

this does not mean that their substantial power is comparable because the parsing order limits the available dynamic features.


To the best of our knowledge, no research has been published on generating the SS given the FS of a Chinese couplet.

because our task can be viewed as generating the second line of a special type of poetry given the first line, we consider automatic poetry generation to be the most closely related existing research area.


Besides the usual local features such as the character-based ones (Xue and Shen, 2003;Ng and Low, 2004), many non-local features related to POSs or words can also be employed to improve performance.

as such features are generated dynamically during the decoding procedure, incorporating these features directly into the classifier results in problems.


This is based on this viewpoint: On the one hand, compared with the initial input character sequence, the pruned word lattice has a quite smaller search space while with a high oracle F-measure, which enables us to conduct more precise reranking over this search space to find the best result.

as the structure of the search space is approximately outlined by the topological directed architecture of pruned word lattice, we have a much wider choice for feature selection, which means that we would be able to utilize not only features topologically before the current considering position, just like those depicted in Table 2 in section 4, but also information topologically after it, for example the next word W 1 or the next POS tag T 1 .


This concept is intuitive when reasoning about the link between syntax and semantics, and it has been used earlier in semantic interpreters such as Absity (Hirst, 1983).

except from a few tentative experiments (Toutanova et al., 2005), grammatical function is not explicitly used by current automatic SRL systems, but instead emulated from constituent trees by features like the constituent position and the governing category.


As can be seen, the constituent-based systems outperform the dependency-based systems on average.

the picture is not clear enough to draw any firm conclusion about a fundamental structural difference.


Again, there are no clear differences that can be attributed to syntactic formalism.

this result is positive, because it shows clearly that SRL can be used in situations where only dependency parsers are available.


For example, from a consumers' forum on digital cameras, the underlined parts in Examples (3) and (4) from Section 1 apparently describe the writer's demands, so they are valuable information for such users of demand analysis such as the makers of digital camera.

the request in Example (5) does not express the author's demands about any digital camera, but rather it is written for other participants in the forum.


As dependency paths contain various words along with nouns and verbs, other methods often mentioned in the literature would be more difficult to use.

in the future we are going to extend this approach by using syntactically analyzed corpora and by estimating distributional similarity from it.


For example, the coordinate structure "((mail and securities) fraud)" is guided by the estimation that mail fraud is a salient compound nominal phrase.

the coordinate structure "(corn and (peanut butter))" is led because corn butter is not a familiar concept.


Since both formulae correspond with a set of animals in the domain, referential ambiguity can result.

the black sheep and goats is shorter and possibly more fluent.


Hence, it seems justifiable to have gre avoid such kind of ambiguities.

it also seems plausible that some readings may be very unlikely.


If a participant removes 2 red lions and 2 red horses, we count it as a wide-scope reading.

if (s)he removes all the horses we count it as a narrow-scope reading.


Active learning is a proven method for reducing the cost of creating the training sets that are necessary for statistical NLP.

there has been little work on stopping criteria for active learning.


The empirical probabilities for the adjustment table are estimated with leave-one-out on the training set.

since the training set is created by selective sampling, it will be biased.


He then suggests to use this peak confidence as the stopping criterion.

in our experiments with multiclass logistic regression, we could not find this peak pattern when calculating the confidence using the three uncertainty measures introduced above: 1-Entropy, Margin and MinMax.


The day after a debate, most papers may declare Bush the winner, yielding a rise in the price of a "Bush to win" share.

while the debate may be discussed for several days after the event, public opinion of Bush will probably not continue to rise on old news.


Expert algorithms for combining prediction systems have been well studied.

experiments with the popular weighted majority algorithm (Littlestone and Warmuth, 1989) yielded poor performance since it attempts to learn the optimal balance between systems while our setting has rapidly shifting quality between few experts with little data for learning.


In each market, the baseline news system makes a small profit, but the overall performance of the combined system is worse than the market history system alone, showing that the news baseline is ineffective.

all news features improve over the market history system; news information helps to explain market behaviors.


", this question can be easily classified into "NUM:distance".

the above SVM misclassified this question into "LOC:-state", as the words "state" and "Alaska" confused the classifier.


Previous summarization tasks are all targeted on a single document or a static collection of documents on a given topic.

the document collections can change (actually grow) dynamically when the topic evolves over time.


That means only the content of the sentences is considered.

for the tasks of queryoriented summarization, the reinforcement should obviously bias to the user's query.


In the process of accumulation, cQA services assume that questions always have unique best answers.

with an indepth analysis of questions and answers on cQA services, we find that the assumption cannot be true.


The best answer chosen by the asker is very good because it provides useful URL information where the asker can find her answers.

other answers listed a variety of birthdays of stars that also answered the question.


if we consider all answers, 7 Inquirer dictionary http://www.wjh.harvard.edu/~inquirer.


Our baseline uses a tight heuristic, requiring aligned words at phrase edges.

ayan and Dorr (2006a) showed that a loose heuristic, allowing unaligned words at the phrase edges, improved accuracy by 3.7 BLEU with some alignments, again with much less training data.


The situation worsens with discontiguous phrases.

with the loose heuristic, we see the opposite effect.


Increasing the maximum phrase length in standard phrase-based translation does not improve BLEU (Koehn et al., 2003;Zens and Ney, 2007).

this effect has not yet been evaluated in hierarchical phrase-based translation.


(2008) address this bottleneck with a promising approach based on parallel processing, showing reductions in real time that are linear in the number of CPUs.

they do not reduce the overall CPU time.


Strictly speaking, the effective projectivity for a particular edit should be computed based on the intermediate form upon which the edit operates, since the projectivity properties of this form can depend on preceding edits.

the Nat-Log system minimizes the need to compute projectivity in intermediate forms by reordering the edits in an alignment in such a way that effective projectivity can, in most cases, simply be taken from the projectivity marking of P and H performed during the linguistic analysis stage.


NatLog sanctions the deletion of a restrictive modifier and an appositive from the premise, and recognizes that deleting a negation generates a contradiction; thus it correctly answers no.

there are many RTE problems where NatLog's precision works against it.


Similarly to the PTB-CFG trees, the simplified HPSG trees do not include empty categories, co-indexing, and function-tags.

we cannot attain a PTB-CFG tree by simply mapping those atomic symbols to the corresponding PTB non-terminal symbols, because the analyses by the PTB-CFG and the HPSG yield different tree structures for the same sentence.


will call the direction of offset (here, respectively, past, past and future).

in other cases there is no explicit indication of the direction of offset from the temporal focus.


(4) Jones met with Defense Minister Paulo Portas on Tuesday and will meet Foreign Minister Antonio Martins da Cruz before leaving Portugal Wednesday.

this approach will not correctly interpret example (10): (10) Still a decision has to made on what, if any, punishment he will face in the wake of that incident Tuesday night.


As the parsing chart is filled in from the bottom up, each rule applied is essentially coming out of a special repair rule set, and so at the top of the tree the EDITED hypothesis is much more likely.

this requires that several fluent speech rules from the data set be modified for use in a special repair grammar, which not only reduces the amount of available training data,   The right corner transform works in a different way, by building up constituent structure from left to right.


Extending this model to actual speech adds some complexity, since disfluency phenomena are difficult to detect in an audio signal.

there are also advantages in this extension, since the extra phonological variables and acoustic observations contain information that can be useful in the recognition of disfluency phenomena.


Assessing language knowledge and competence quantitatively is not a novel concept in second language learning assessment.

the application of data mining methods to automatically assess language proficiency in a discourse setting is novel.


In the STD group, we see that four feature sets, all, turns, wordBased and patient equal or surpass the baseline F-score.

to this, upon examination of the performance of the classifiers built over the BC dataset, we do not observe any improvements over the baseline and the results are markedly worse than those for the combined dataset.


The examiners' subjectivity of overall performance is minimised by the highly structured examination setup and well-defined assessment criteria.

as shown in Table 3, the communicative style of the SP is a contributing factor to the perception of successful clinical and communication skills.


Powell's method is not designed to reach a better local optimum than co-ordinate ascent, but does have convergence guarantees under certain idealized conditions.

we have observed informally that, in MERT, co-ordinate ascent always seems to converge relatively quickly, with Powell's method offering no clear advantage.


Without random restarts, this will guarantee convergence because the last set of feature weights selected will still be a local optimum.

2 if we go through the coordinate ascent procedure without finding a better set of feature weights, then we do not have to perform the last iteration of nbest decoding, because it will necessarily produce the same n-best lists as the previous iteration, as long as the decoder is deterministic.


This is due to the double matching problem: for example, pair "Tehran Times (Tehran)"/"Inter Press Service" (from INT) is scored more than 1.0 because "Tehran" matches "Inter" twice: even with a low score as a coefficient, "Inter" has a high IDF compared to "Press" and "Service", so counting it twice makes normalization wrong.

this problem may be solved by choosing a more adequate sub-measure: experiments show that using the CosTFIDF measure with bigrams or trigrams outperforms standard CosT-FIDF.


Although Model II reproduces the structural properties of PlaNet and PhoNet quite accurately, as we shall see shortly, it fails to generate inventories that closely match the real ones in terms of feature entropy.

at this point, recall that Model II assumes that the consonant nodes are unlabeled; therefore, the inventories that are produced as a result of the synthesis are composed of consonants, which unlike the real inventories, are not marked by their distinctive features.


and "A young boy and his mother were found dead on Wednesday evening.".

it also needs to detect complex cases like: "An ambulance rushed the soldier to hospital, but efforts to save him failed."


It seems intuitive that a naïve system that selects only sentences that contain terms with senses connected with death like "kill", "die" or "execute" as positive instances would catch many positive cases.

there are instances where this approach would fail.


The overall goal of the TDT Event Tracking task was to track the development of specific events over time.

these TDT tasks were somewhat restrictive in the sense that detection is carried out at document level.


As a result, the probability of a sentence is the product of the probabilities of its terms.

we calculate the probability that a given test sentence s k belongs to class c i as follows: this model will generally underestimate the probability of any unseen word in the sentence, that is terms that do not appear in the training data used to build the language model.


In general, the language modeling based tech-niques are not as effective as the SVM approach for this classification task.

from Table 4 we see that all language models achieve approx.


Thus, a term like "professor" that may only occur once in the dataset has the same likelihood of occurring in an "on-event" sentence as a term like "kill" that has a very high frequency in the dataset.

the Jelinek-Mercer and Absolute Discounting smoothing methods estimate the probability of unseen terms according to a background model built using the entire collection.


96%, 95%, 86% and 60% "off-event" F1 scores respectively across all event types.

figure 2 demonstrates that the performance of each approach for the "onevent" class varies considerably across the event types.


For instance, the trigger-based classification baseline out-performs all other approaches achieving over 60% F1 score for the "Meet", "Die" and "Charge-Indict" types.

for events like "Attack" and "Transport" this baselines F1 score drops to approx.


Event types where it achieves poor results are broader types like "Transport" and "Attack" that cover a larger spectrum of event instances from heterogeneous contexts and situations.

we see from Figure 2 that the SVM performs well on such event types and as a result out-performs the trigger-based selection process by approximately a factor of 4 for the "Attack" event and a factor of 2 for the "Transport" event.


We find that the F1 scores of the "off-event" class are not affected much.

the F1 scores for the "on-event" class for the SVM and trigger-based baseline are reduced by margins of approx.


the success of this baseline is somewhat dependent on the nature of the event in question.


This system resolves anaphoric pronouns by using heuristic rules and seven patterns for parallelism.

the sizes of the data sets used in their experiments were small.


Such methods were claimed to be comparable with traditional methods.

the problems caused by domain differences, which strongly affect a deep-semantics related task like pronoun resolution, have not yet been studied well enough.


In addition to these types of pronouns, the annotated corpora contain other types of pronominal anaphora, including "both," "one," numeric mentions (GE-NIA), and bound anaphora (ACE).

analysis statistics show that such pronouns occupy less than 5% of the total pronouns in the GENIA corpus, thus we have ignored them.


Some support for this view can be found in the results from the CoNLL shared tasks on dependency parsing in 2006 and 2007, where a variety of data-driven methods for dependency parsing have been applied with encouraging results to languages of great typological diversity (Buchholz and Marsi, 2006;Nivre et al., 2007a).

there are still important differences in parsing accuracy for different language types.


Comparing the use of raw word forms (LEX) and lemmas (LEM) as lexical features, we see a slight advantage for the latter, at least for labeled accuracy.

it must be remembered that the experiments are based on gold standard input annotation, which probably leads to an overestimation of the value of LEM features.


It is also worth mentioning that phantom tokens, i.e., empty tokens inserted for the analysis of certain elliptical constructions (see section 2), have a labeled precision of 82.4 and a labeled recall of 82.8 (89.2 and 89.6 unlabeled), which is very close to the average accuracy, despite being very infrequent.

it must be remembered that these tokens were given as part of the input in these experiments.


The feasibility of this approach was demonstrated by Gordon and Swanson (2007) for syntactically similar verbs.

their approach requires at least one annotated instance of each new predicate, limiting its practicability.


Therefore, it is necessary to consider syntactic features as well.

these vary substantially between verbs and nouns.


Example 3 shows that an "external" NP like Bill can be analysed as filling the HELPER role of the noun help.

the overall proportion of non-local roles is still fairly small in our data (around 10%).


Caution should be taken when interpreting the results for n < 3 since the annotator agreement for these was very low.

as shown in Figure 1, human preference for the JIR model was higher at n ≥ 3.


This dependency relationship offers a very condensed representation of the information needed to assess the relationship in the forms of the dependency tree (Culotta and Sorensen, 2004) or the shortest dependency path (Bunescu and Mooney, 2005) that includes both entities.

when the parse tree corresponding to the sentence is derived using derivation rules from the bottom to the top, the wordword dependencies extend upward, making a unique head child containing the head word for every non-terminal constituent.


It shows that their model can guess the POS for disyllabic words with a relatively good F-measure (83.60%).

the recall is not high for disyllabic (79.11%) and tri-syllabic (82.70%) words, and quite low for four-character (20.95%) and fivecharacter (0%) words.


Notice that the training data was formed by segmenting and tagging POS of each word in a dictionary using an existing tool like ICTCLAS.

these tools usually generate quite a few errors on the words, because they are designed to handle sentence but not word.


In fact, Fellbaum (1998) allows for more than one unique beginner per verb category.

cases where there is a large number of unique beginners in one category merit investigation.


Besides the sheer size of these data sets, the main attraction of user logs lies in the possibility to capitalize on users' input, either in form of user-generated query reformulations, or in form of user clicks on presented search results.

noisy, sparse, incomplete, and volatile these data may be, recent research has presented impressive results that are based on simply taking the majority vote of user clicks as a signal for the relevance of results.


SMT-based expansions such as henry viii restaurant portland, maine, or ladybug birthday ideas, or top ten restaurants, vancouver achieve a change in retrieval results that does not result in a query drift, but rather in improved retrieval results.

the terms introduced by the correlation-based system are either only vaguely related or noise.


Furthermore, each component of the SMT model takes great care to avoid sparse data problems by various sophisticated smoothing techniques.

the correlation-based model relies on pure counts of term frequencies.


If w i ∈ S 1 , then under the assumption that incomplete edges are extended from left-toright (see footnote 1), the incomplete edge should  be discarded, because any completed edges that could result from extending that incomplete edge would have the same start position, i.e., the chart cell would be (i, k) for some k>i, which is closed to the completed edge.


Informally, we define a projective DAG to be a DAG where all arcs can be drawn above the sentence (written sequentially in its original order) in a way such that no arcs cross and there are no covered roots (although a root is not a concept associated with DAGs, we borrow the term from trees to denote words with no heads in the sentence).

nonprojectivity is predictably more wide-spread in DAG representations, since there are at least as many arcs as in a tree representation, and often more, including arcs that represent non-local relationships.


Agent, Patient, Location and Instrument.

propBank and FrameNet use a mixture of role types: some are common amongst a number of frames; others are specific to particular frames.


Biology-specific extensions have been attempted both for PropBank (Wattarujeekrit et al., 2004) and FrameNet (Dolbey et al., 2006).

to our knowledge, there has been no such attempt at extending VerbNet into the biological domain.


only a few semantic argument types are annotated.


Our event patterns are similar to information extraction rules used in conventional IE systems.

the goal of this paper is not event instance extraction but event (or semantic) frame extraction.


They use the case frames as selectional restriction for zero pronoun resolution, but do not utilize the frequency of each example of case slots.

since the frequency is shown to be a good clue for syntactic and case structure analysis (Kawahara and Kurohashi, 2006), we consider the frequency also can benefit zero pronoun detection.


Pronoun P (n jm |CF l , s j , A =1) is similar to P (n j |CF l , s j , A=1) and can be estimated approximately from case frames using the frequencies of case slot examples.

while A (s j ) = 1 means s j is not filled with input case component but filled with an entity as the result of zero anaphora resolution, case frames are constructed by extracting only the input case component.


Kawahara and Kurohashi's model achieved almost 50% as F-measure against newspaper articles.

as a result of our experiment against web documents, it achieved only about 20% as F-measure.


Although compiling such resources is labor intensive and achieving wide coverage is difficult, these resources to some extent explicitly capture semantic structures of concepts and words.

corpus statistics achieve wide coverage, but the semantic structure of a concept is only implicitly represented in the context.


Actually, same relations holding between entities often involve co-reference (where co-reference is broadly conceived to include relations such as part-whole listed above).

there are no morphosyntactic constraints on what targets may be.


For instance, Example 4 is characterized by mixtures of opinion types, polarities, and target relations.

the opinions are still unified in the intention to argue for a particular type of shape.


The opinion frames flesh out the discourse relations: we have lists specifically of positive sentiments toward related objects.

opinion-frame and discourse-relation schemes are not redundant.


Our results are encouraging -even using simple features to capture target relations achieves considerable improvement over the baselines.


Evidence from the surrounding context has been used previously to determine if the current sentence should be subjective/objective (Riloff et al., 2003;Pang and Lee, 2004) and adjacency pair information has been used to predict congressional votes (Thomas et al., 2006).

these methods do not explicitly model the relations between opinions.


Parallel text is used as training data with the alternative translations serving as sense labels.

disadvantages of this approach are that the alternative translations do not always correspond to the sense distinctions in the original language and parallel text is not always available.


For 11 terms training using the additional examples alone is more effective than using the NLM-WSD corpus.

there are several words for which the performance using the automatically acquired examples is considerably worse than using the NLM-WSD corpus.


We show that results achieved with existing resources that are not tailored towards word sense subjectivity classification can rival results achieved with supervision on a manually annotated training set.

results with different resources vary substantially and are dependent on the different definitions of subjectivity used in the establishment of the resources.


It is also true that the sentences (plot descriptions) in its "objective" data set relatively rarely contain opinions about the movie.

they still contain other opinionated content like opinions and emotions of the characters in the movie such as the obsession of a character with John Lennon in the beatles fan is a drama about Albert, a psychotic prisoner who is a devoted fan of John Lennon and the beatles.


The effectiveness of the resulting algorithms depends greatly on the generated training data, more specifically on the different definitions of subjectivity used in resource creation.

we were able to show that at least one of these methods (based on the SL word list) resulted in a classifier that performed on a par with a supervised classifier that used dedicated training data developed for this task (CV).


Compared with table 1 on page 4, table 5 and  table 6 both indicate the predicted MP can help to label semantic roles.


For testing, given a new test sequence x, we want to estimate the most probable label sequence (Best Label Path), y * , that maximizes our conditional model: In the CRF model, y * can be simply searched by using the Viterbi algorithm.

for latent conditional models like LDCRF, the Best Label Path y * cannot directly be produced by the Viterbi algorithm because of the incorporation of hidden states.


Most unsupervised rule learning algorithms focused on learning binary entailment rules.

using binary rules for inference is not enough.


Given a dependency parse tree, any sub-tree can be a candidate template, setting some of its nodes as variables (Sudo et al., 2003).

the number of possible templates is exponential in the size of the sentence.


They suggest that for certain error annotation tasks, such as preposition usage, it may not be appropriate to use only one rater and that if one uses multiple raters for error annotation, there is the possibility of creating an adjudicated set, or at least calculating the variability of the system's performance.

annotation with multiple raters has its own disadvantages as it is much more expensive and timeconsuming.


, x S }, which is a literal representation of the document.

it can also be defined in terms of its information content.


For example, birthday appears more often with joy, while war appears more often with fear.

the accuracy achieved by their method is not practical in applications assumed in this paper.


For example, in the case of There are a lot of enemies in Table 4, the "Polarity" is Correct because it represents a negative emotion.

its emotion class unpleasantness is judged Context-dep.


From these results, we speculate that, as far as the two-class sentiment polarity problem is concerned, word n-gram features might be sufficient if a very large set of labelled data are available.

table 8 indicates that the three-class problem is much harder than the twoclass problem.


(2007)'s method is that it does not require any manual supervion.

our models, which rely on emotion-provoking event instances, are also totally unsupervised -no supervision is required to collect emotion-provoking event instances.


Most of the corpora that are well-known in NLP communities are completely-annotated in general.

it is quite common that the available annotations are partial or ambiguous in practical applications.


This feature is suited to many NLP tasks that include correlations between elements in the output structure, such as the interrelation of part-of-speech (POS) tags in a sentence.

conventional CRF algorithms require fully annotated sentences.


A CRF that was trained using only the source domain corpus (A), CRF S , achieved F =96.84 in the source domain validation data (B).

it showed the need for the domain adaptation that this CRF S suffered severe performance degradation (F =92.3) on the target domain data.


In this experiment, we used 118 sentences in which some words (82 distinct words) are annotated with ambiguous POS tags, and these sentences are called the POS ambiguous sentences.

we call sentences in which the POS tags of these terms are uniquely annotated as the POS unique sentences.


It might seem that a lexicon, such as Word-Net (Fellbaum, 1998), contains all the information we need to handle these four tasks.

we prefer to take a corpus-based approach to semantics.


For the second two experiments, PairClass performs significantly above the baselines.

the strength of this approach is not its performance on any one task, but the range of tasks it can handle.


• The morphological processing in PairClass (Minnen et al., 2001) is more sophisticated than in Turney (2006).

we believe that the main contribution of this paper is not PairClass itself, but the extension of supervised word pair classification beyond the classification of noun-modifier pairs and semantic relations between nominals, to analogies, synonyms, antonyms, and associations.


As the marker-based classification only used overuse and the necessary degree of overuse is lower for Linguistic Profiling, the latter pays attention to many more features and should be able to attain a better classification rate.

if we want to determine which features are most powerful, interpreting the workings of Linguistic Profiling will be more difficult than for the marker-based approach.


The Dutch (or Flemish) parliamentarians announce in some way that they are nearing the end of their speech.

if we examine the original Dutch text, we observe a much more varied phrasing.


We have not examined POS classes, but only specific words.

we do see various adverbs in prominent positions, especially in Table 7, which indeed shows overuse.


In all, we extract talking points of the form is adj:noun for over 40,000 WordNet concepts, and talking points of the form verb:noun for over 50,000 concepts.

the real power of talking points emerges when they are connected to form a slipnet, as we discuss in section 4.


There are eleven questions for which the reference answer was ranked in the top 10 by the baseline system but it drops out of the top 10 by re-ranking.

there are 22 questions for which the reference answer enters the top 10 by re-ranking the answers, leading to an overall improvement in success@10.


68.1%, for a resulting F-measure of 63.1%.

adding the binding kernel (PK+STK) leads to a significant improvement in 17% precision for MUC-6 with a small gain (1%) in recall, whereas on the ACE data set, it also helps to increase the recall by 7%.


We can see that when λ∈(0.5,1), the RankFusion methods with high-quality clusters can outperform both the corresponding SingleRank and the corresponding CollabRank.

the performance improvements of RankFusion over CollabRank are not significant.


Due to the limitations in natural language processing technology, abstractive approaches are restricted to specific domains.

extractive approaches commonly select sentences that contain the most significant concepts in the documents.


However, only moderate results were reported.

dejong (1978) represented documents using predefined templates.


The trained SMT systems are suitable for translating texts in the same domain as the training corpus.

for some specific domains, it is difficult to obtain a bilingual corpus.


It has been shown that there is no faster way for solving the all-pairs shortest paths problem in general graphs.

the structure of wordnets is somewhat different to that of a general directed graph.


For example, (Gao et al., 2005) described an adaptive CWS system, and (Andrew, 2006) employed a conditional random field model for sequence segmentation.

these methods are not specifically developed for the MT application, and significant improvements in translation performance need to be shown.


In practice we do not re-normalize the probabilities and our model is thus deficient because it does not sum to 1 over valid observations.

we found the model work very good in our experiments.


Domain specific term and after these term succ sors of the terms, are likely to be either functional words or other general substantives cting terms successors can be considered as markers of terms, and are referred to as term delimiters in this paper.

to terms, delimiters are relatively stable and domain independent.


Thus, TF-IDF performs relatively well because of the high-quality domain corpus.

tF-IDF, as a statistics based algorithm suffers from similar problem as others based on statistics.


Thus, new terms are actually ranked higher than other terms in TV_ConSem which explains its higher ability to identify new terms in the low range of N TCList .

its performance drops in the high range of N TCList because the influence of context words diminishes in terms of percentage in the domain lexicon to distinguish terms from non-terms.


The altered metric aims to capture the visual and semantic salience of radicals in kanji perception, and to also take into account some basic shape similarity.

to the previous approach, we can consider kanji as arbitrary symbols rendered in print or on screen, and then attempt to measure their similarity.


In Figure 5, the baseline parser incorrectly assigned 签订/NN (signing) as the head of 合作/NN (cooperation).

after using the case element 项目/NN (project) 合作/NN, the correct head of 合作/NN was found by the proposed parser.


Gold standard word segmentation and pos-tag are applied in previous experiments.

parsing accuracy will be affected by the incorrect word segmentation and pos-tag in the real applications.


Relation extraction was treated as a sequence labeling problem and relational patterns were discovered to boost the performance.

this model extracts relations without considering dependencies between entities, and the best reported F-measure is 67.91, which is significantly (by 2.5%) lower than our MLN system when evaluated on the same training and testing sets.


Engström (2004) reports decreased accuracy in cross-domain classification since sentiment in different domains is often expressed in different ways.

it is impossible in practice to have annotated data for all possible domains of interest.


Unaligned words in either language (an empty row or column in the alignment matrix, not present in our example) will be attached as high as possible in our tree.

other ways of handling unaligned words are possible given the decomposition tree.


Many reordering approaches have been proposed for the statistical machine translation (SMT) system.

the information about the type of source sentence is ignored in the previous works.


reordering is always a key issue in the decoding process.


A chunk-level reordering model was first proposed by .

all the existing models make no distinction between the different types of the source sentence.


Obviously, the Chinese question phrase "什 么样 的 座位 (What kind of seats)" should be put at the beginning of its English translation.

many phrase-based systems fail to do this.


The fundamental assumption underlying much recent work on syntax-based modeling, which is considered to be one of next technology breakthroughs in SMT, is that translational equivalence can be well modeled by structural transformation.

as discussed in prior arts (Galley et al., 2004) and this paper, linguistically-informed SCFG is an inadequate model for parallel corpora due to its nature that only allowing child-node reorderings.


More recently, the hidden Markov support vector machines (HM-SVMs) (Altun et al., 2003) have been proposed which combine the flexibility of kernel methods with the idea of HMMs to predict a label sequence given an input sequence.

hM-SVMs require full annotated corpora for training which are difficult to obtain in practical applications.


However, HM-SVMs require full annotated corpora for training which are difficult to obtain in practical applications.

the HVS model can be easily trained from only lightly annotated corpora.


In uncertainty sampling scheme, the most uncertain unlabeled example is considered as the most informative case selected by active learner at each learning cycle.

an uncertain example for one classifier may be not an uncer-tain example for other classifiers.


The motivation behind the overall-uncertainty method is similar to that of the max-confidence method.

the max-confidence method only considers the most informative example at each learning cycle.


(2008) proposed a minimum expected error strategy to learn a stopping criterion through es-timation of the classifier's expected error on future unlabeled examples.

both two studies did not give an answer to the problem of how to define an appropriate threshold for the stopping criterion in a specific task.


Vlachos (2008) also studied a stopping criterion of active learning based on the estimate of the classifier's confidence, in which a separate and large dataset is prepared in advance to estimate the classifier's confidence.

there is a risk to be misleading because how many examples are required for this pregiven separate dataset is an open question in real-world applications, and it can not guarantee that the classifier shows a rise-peak-drop confidence pattern during active learning process.


In previous studies on active learning, the initial training set is generally generated by random sampling from the whole unlabeled corpus.

random sampling technique can not guarantee selecting a most rep-resentative subset, because the size of initial training set is generally too small (e.g.


We think selecting examples from each cluster can alleviate the redundancy problem.

this sampling scheme works poorly for WSD and TC on the three data sets, compared to traditional uncertainty sampling.


Actually these proposed techniques can be easily applied for committee-based sampling for active learning.

to do so, we should adopt a new uncertainty measurement such as vote entropy to measure the uncertaity of each unlabled example in committee-based sampling scheme.


While Bleu is a reasonable choice for evaluating the quality of the overall composite set of translation sentences, it is not suitable for sentence-level decisions.

in line with Nomoto (2003)'s motivation for developing m-precision as an alternative to Bleu, we make the following observation.


This means it is a measure for how much the occurrence of word A makes the occurrence of word B more likely, which we term positive association, and how much the absence of word A makes the occurrence of word B more likely, which we term negative association.

our experiments show that only positive association is beneficial for aligning words cross-lingually.


We can see that a query and its translation shares some pivots which are associated with statistical significance 20 .

it also illustrates that the actual LLR value is less insightful and can hardly be compared across these two corpora.


In Table 6, we see that the first query "gear" 21 is highly associated with "shift".

on the English side we see that gear is most highly associated with the pivot word gear.


In summary, we can see that in both cases the degree of associations are rather different, and cannot be compared without preprocessing.

it is also apparent that in both examples a simple L1 normalization of the degree of associations does not lead to more similarity, since the relative differences remain.


Section 1); and (ii) even a single sentence could be considered a case of plagiarism, as it transmits a complete idea.

a plagiarised sentence is usually not enough to automatically negate the validity of an entire document.


Our hypothesis is that whenever there is a lexical class motivated by a particular distributional behaviour, a learner can be trained to identify the members of this class.

there are two main problems to lexical classification: noise and silence, as we will see in section 4.


Brent's hypothesis, followed by most authors afterwards, is that noise can be eliminated by statistical methods because of its low frequency.

the fact is that in our test set significant information is as sparse as noise, and the DT cannot correctly handle this.


This was the main reason to introduce negative contexts as well as positive ones, as we mentioned in section 3.

these systematic sources of error can be taken as an advantage when assessing the usability of the resulting resources.


Sample similarity in the multi-way sentiment detection setting has previously been considered by using Support Vector Machines (SVMs) in conjunction with a metric labeling metaalgorithm (Pang and Lee, 2005); by taking a semisupervised graph-based learning approach (Goldberg and Zhu, 2006); and by using "optimal stacks" of SVMs (Koppel and Schler, 2006).

each of these methods have shortcomings (Section 2).


This problem may be addressed by considering SVM regression (SVM-R) (Smola and Schölkopf, 1998), where class labels are assumed to come from a discretisation of a continuous function that maps the feature space to a metric space.

sVM-R, like the sVM schemes described here, trains on the entire feature set for all the classes in the dataset.


We see that for high β, there are few n-grams with p(u|E) ≥ β; this is as expected.

even at a high threshold of β = 0.9 there are still on average three 4-grams per sentence with posterior probabilities that exceed β.


favour the shorter but disfluent hypothesis; normalising by length was not effective.

the stupid-backoff LM has better coverage and the backing-off behaviour is a clue to the presence of disfluency.


The only exceptions are articles without an infobox which cannot be used for training.

this is not a real issue because the amount of remaining data is sufficient: 9000 articles can be used for this task.


The complexity of the parsing algorithm is usually considered the reason for long parsing times.

it is not the most time consuming component as proven by the above analysis.


This provides an advantage for small training corpora.

this is probably not the main reason for the high improvement, since for languages with only slightly larger training sets such as Chinese the improvement is much lower and the gradient at the end of the curve is so that a huge amount of training data would be needed to make the curve reach zero.


The scores for Catalan, Chinese and Japanese are still lower than the top scores.

the parser would have ranked second for these languages.


The figures are not directly comparable since HALogen takes as input syntactic structures.

it gives us an idea where our generator is situated.


In previous studies in IR, decaying weight along with distance within a text window have been proposed.

the decaying functions are defined manually.


If we consider the question Q 24 below as reference, question Q 26 will be deemed more useful than Q 25 when using cos or mcs because of the higher relative lexical and conceptual overlap with Q 24 .

this is contrary to the actual ordering Q 25 ≻ Q 26 |Q 24 , which reflects that fact that Q 25 , which expects the same answer type as Q 24 , should be deemed more useful than Q 26 , which has a different answer type.


The analysis above shows the importance of using the answer type when computing the similarity between two questions.

instead of relying exclusively on a predefined hierarchy of answer types, we have decided to identify the question focus of a question, defined as the set of maximal noun phrases in the question that corefer with the expected answer.


It does not take any information of other mentions into account.

it turned out that it is difficult to improve upon their results just by applying a more sophisticated learning method and without improving the features.


Since flat-K partitioning did not perform as well we focus here on recursive 2way partitioning.

to flat-K partitioning, this method does not need any information about the number of target sets.


The CEAF-algorithm aligns entities in key and response by means of a similarity metric, which is motivated by B 3 's shortcoming of using one entity multiple times (Luo, 2005).

although CEAF theoretically does not require to have the same number of mentions in key and response, the algorithm still cannot be directly applied to end-to-end coreference resolution systems, because the similarity metric is influenced by the number of mentions in key and response.


We implemented distance as weights on hyperedges which resulted in decent performance.

this is limited to pairwise relations and thus does not exploit the power of the high degree relations available in COPA.


lexical or structural) of the target sentences.

in recognizing relations, humans are not thus constrained and rely on an abundance of implicit world knowledge or background information.


What quantifies as world or background knowledge is rarely explored in the RE literature and we do not attempt to provide complete nor precise definitions in this paper.

we show that by considering the relationship between our relations of interest, as well as how they relate to some existing knowledge resources, we improve the performance of RE.


(2008) took advantage of the hierarchical ontology of relations by proposing methods customized for the perceptron learning algorithm and support vector machines.

we propose a generic way of using the relation hierarchy which at the same time, gives globally coherent predictions and allows for easy injection of knowledge as constraints.


More people used predicate senses in semantic role labeling (Hajič et al., 2009;Surdeanu et al., 2008).

both of the pipeline methods ignore possible dependencies between the word senses and semantic roles, and can result in the error propagation problem.


Besides jointly learning semantic role assignment of different constituents for one task (semantic role labeling), their methods have been used to jointly learn for two tasks (semantic role labeling and syntactic parsing).

it is easy for the re-ranking model to loss the optimal result, if it is not included in the top n results.


The semantic role labeling not only can help predicate sense disambiguation, but also argument sense disambiguation (a little).

because of the limitation of the pipeline model, it is difficult to make semantic role labeling to help predicate and argument sense disambiguation simultaneously.


First, most works identify the polarity of adjectives and adverbs because the syntactic constructs generally express sentimental semantics.

our method identifies the polarity of person names.


For instance, knowing the existence of an emotion is often insufficient to predict future events or decide on the best reaction.

if the emotion cause is known in addition to the type of emotion, prediction of future events or assessment of potential implications can be done more reliably.


the hyper-plane is used to separate positive and negative instances during classification process without consideration of the margin.


Normally, parsing is done at sentence level.

in many cases a pronoun and its antecedent candidate do not occur in the same sentence.


It is same as our strategy without negative instances from nonevent anaphoric pronouns.

our study showed an improvement by adding in negative instances from non-event anaphoric pronouns as showed in table 4.


Synthesis works better for subjects in Person category, because the biographical structure provides a specific and fairly unrelated content in each section, making the synthesis less redundancy-prone.

there is arbitrariness when organizing articles in Event and Culture category.


One of the aims of this grammar is to be precision-oriented: it tries to give detailed analyses of the German language, and reject ungrammatical sentences as much as possible.

this precision comes at the cost of lower coverage, as we will see later in this paper.


The intuition is that when the restricted part of the grammar can find a solution, that solution will indeed be found, and preferred by the statistical models.

when the sentence is extragrammatical, the robustness rules may be able to overcome the barriers.


The former is hard to establish, because of the missing lexical item.

the latter should be doable: the lexicon knows that 'yesterday' is an adverb that modifies verbs.


As expected, pivot translations yield lower quality scores than the corresponding direct translations hypotheses.

pivot hypotheses may contain better lexical predictions, that the additional model helps transfer into the baseline system, yielding translations with a higher quality, as shown in many cases the +auxLM systems results.


As can be seen, the (f r → de) system is still improved by using the additional language model.

the absolute value of the gain under the full condition (+0.61) is lower than that of the intersection data condition (+0.96).


This could be explained, in addition to the lower Bleu3 and Bleu4 precision, by the fact that the quality of the translation of grammatical words may have decreased.

italian brings little improvement for content words save for nouns.


Both deal with multilingual information extracted from the Web.

the majority of CLIR studies pursue different targets.


As discussed in Section 3.1, we dismissed terms for which no translation was found in any of the available dictionaries, so each term in each of the obtained pairs has at least a single translation to the target language.

in many cases the available translations represent the wrong word sense, since both the source terms and their translations can be ambiguous.


Then we use the pointwise mutual information between a noun phrase and an opinion word to measure the association.

this PMI value cannot be encoded directly as a feature as it only captures the local information between antecedent candidates and opinion words.


Thus we did not use the following features: semantic class agreement features, the gender agreement feature, and appositive feature.

we added some specific features, which are based on two extracted entities, i and j , where i is the potential antecedent and j is the potential anaphor: Is-between feature: Its possible values are true and false.


(2008) built a cross document coreference system using features from encyclopedic sources like Wikipedia.

successful coreference resolution is insufficient for correct entity linking, as the coreference chain must still be correctly mapped to the proper KB entry.


Most readers will immediately think of the 42nd US president.

the only two William Clintons in Wikipedia are "William de Clinton" the 1st Earl of Huntingdon, and "William Henry Clinton" the British general.


All of the above features are general for any KB.

since our evaluation used a KB derived from Wikipedia, we included a few Wikipedia specific features.


The performance of ranking decreases with a significance level of 0.05 when removing it from the best feature combination.

other features do not show significant contribution.


All work mentioned above share a common setting: an MBR decoder is built based on one and only one MAP decoder.

recent research has shown that substantial improvements can be achieved by utilizing consensus statistics over multiple SMT systems (Rosti et al., 2007;Li et al., 2009a;Li et al., 2009b;Liu et al., 2009).


Indeed, a link between a finite verb and an article does not correspond to any grammatical relation between the two.

the premise for our work is that redundancy should be sufficient to identify not only important words but also salient links between words.


This procedure is similar to the one used by Barzilay & Lee (2003) in that we also first identify "backbone nodes" (unambiguous alignments) and then add mappings for which several possibilities exist.

they build lattices, i.e., directed acyclic graphs, whereas our graphs may contain cycles.


For example, if some sentences speak of president Barack Obama or president of the US Barack Obama, and some sentences are about president Obama, we want to add some reward to the edge between president and Obama.

longer paths between words are weak signals of word association.


In particular, we use the Viterbi algorithm to find the sequence of words of a predefined length n which maximizes the bigram probability (MLEbased): Similar to the shortest path implementation, we specify compression length and set it also here to eight tokens.

the compressions obtained with this method are often unrelated to the main theme.


An interesting difference in the performance for Spanish and English is that shortest path generates more grammatical sentences than the improved version of it.

the price for higher grammaticality scores is a huge drop in informativity: half of such summaries are not related to the main theme at all, whereas 40% of the summaries generated by the improved version got the highest rating.


One may notice that the summaries produced by the baseline are shorter than those generated by the shortest paths which might look like a reason for its comparatively poor performance.

the main source of errors for the baseline was its inability to keep track of the words already present in the summary, so it is unlikely that longer sequences would be of a much higher quality.


Perhaps the work of Barzilay & Lee (2003) who align comparable sentences to generate sentencelevel paraphrases seems closest to ours in that we both use word graphs for text generation.

this is a fairly general similarity, as both the goal and the implementation are different.


for LNK, we use all the remaining relations, namely hypernyms, domains, etc.


With polysemous words (PC, PA), expansion works more effectively, and helps to supply appropriate images for each sense.

with MC, both LNK and SYN have less precision.


(2009) undertook expansion using hypernyms and this may be an appropriate way to obtain many more images for each sense.

because our aim is employ several suitable images for each sense, high precision is preferable to high recall.


Now, we focus on LNK shared by Lexeed, and then we analyze the reasons for F (Table 10).

to Lexeed, no sense is classified as "difficult to portray the sense using images".


Thus, the positional information is not required nor is it maintained.

we maintain positional information at each node as this is critical for the selection of candidate paths.


These methods are proven to be effective in improving the quality of alignments.

the discriminative training in these methods is restricted in using the model components of generative models, in other words, incorporating new features is difficult.


For simpler models such as Model 1 and Model 2, it is possible to obtain sufficient statistics from all possible alignments in the E-step.

for fertility-based models such as Models 3, 4, and 5, enumerating all possible alignments is NP-complete.


On the one hand, two constituents are combined by the algorithm's inference rules only if they cover disjoint parts of the input semantics.

the semantic indices present in both the input formula and the lexically retrieved RTG trees are used to prevent the generation of intermediate structures that are not compatible with the input semantics.


An ideal metric would be the actual BLEU score that the system would obtain under this reordering rule on the development set.

since each rule affects word alignment, phrase extraction, optimal feature weights, and the actual translation, it would be necessary to retrain the entire phrase-based system for each possible rule, which is impractical.


While genetically related languages tend to have similar typological features as they could inherit the features from their common ancestor, they could also differ a lot due to language change over time.

languages with no common ancestor could share many features due to language contact and other factors.


Table 9 shows that MADA produces a high quality segmentation, and that the effect of cascading segmentation errors on parsing is only 1.92% F1.

mADA is language-specific and relies on manually constructed dictionaries.


It has been observed that words are similar if their contexts are similar (Freitag et al., 2005) and so synonymy detection has received a lot of attention during the last decades.

words used in the same context are not necessarily synonyms and can embody different semantic relationships such as hyponyms, meronyms or co-hyponyms (Heylen et al., 2008).


One obvious way to verify all the possible connections between words of the vocabulary employs an exhaustive search.

comparison based on word usage can only highlight those terms that are highly similar in meaning.


To obtain a paraphrase corpus, we compute all sentence pairs similarities Sumo(S a , S b ) and select only those pairs exceeding a given threshold, in our case threshold = 0.85, which is quite restrictive, ensuring the selection of pairs strongly connected 3 .

to take into account the normalization of the corpus, little adjustments had to be integrated in the methodology proposed in (Cordeiro et al., 2007a).


Documents are generated only based on a BOW assumption.

word order information is very important for most textrelated tasks, and simply discarding the order information is inappropriate.


grammatical relations) to form the virtual words.

we do not incorporate all the dependencies.


On the other hand, it is common for researchers to rephrase and republish their research, tailoring it for different academic journals and conference articles, to disseminate their research to the widest possible interested public.

these researchers must include in each publication a meaningful or an important portion of a new material (Wikipedia, 2010).


That is, the CA and CR are very close in their quailty to the best methods.

the CA and the CR have a clear advantage on the other methods.


Especially CA and also CR are among the best methods for identification of various levels of plagiarism.

to the best full and selective fingerprint methods, CA and CR check a rather small portion of the papers, and therefore, their run time is much more smaller.


As the previous discussion has shown, Wordnet-LMF provides a very useful basis for converting GermaNet into LMF.

a number of modifications to Wordnet-LMF are needed if this conversion is to preserve all information present in the original resource.


In WordNet both syntactic frames and examples are linked to synsets.

at least in the case of syntactic frames the linkage to synsets seems problematic since different members of the same synset may well have different valence frames.


Although the acquisition of bilingual data also targets news text, the noisy mined corpus can never compete with the well prepared B1 dataset.

the NIST 2008 test set contains a large portion of out-of-domain text, and so the B1 set does not gain any advantage over Web mined corpora.


In this case, the CL-HYB approach simply chose to group together entities having clicks to 'aaa.com' and appearing in contexts as 'auto club'.

cL-Web grouped according to contexts such as 'selling' and 'company'.


All our systems return a high number of nonobvious suggestions (all above 50%).

6 GOO and YAH show low performance, as both systems are heavily based on the substring matching technique.


Examples (1) and (2) show two sentences from the MPQA corpus where DSEs  The task of marking up these expressions has usually been approached using straightforward sequence labeling techniques using using simple features in a small contextual window (Choi et al., 2006;Breck et al., 2007).

due to the simplicity of the feature sets, this approach fails to take into account the fact that the semantic and pragmatic interpretation of sentences is not only determined by words but also by syntactic and shallow-semantic relations.


there are also differences compared to typical argument extraction in SRL.


The most visible effect of the reranker is that the recall is greatly improved.

this does not seem to have an adverse effect on the precision until the candidate set size goes above 16 -in fact, the precision actually improves over the baseline for small candidate set sizes.


We can see that the estimates lead to a slightly   Figure 13: Items produced by the parser lower F-score.

while the losses in terms of F 1 are small, the gains in parsing time are substantial, as Fig.


Secondly, we note that all of the above approaches that use language models train a language model for each difficulty level using the training data for that level.

since the amount of training data annotated with levels is limited, they can not train higher-order language models, and most just use unigram models.


(2009) has shown that it is possible to project the parameters learnt from the annotation work of one language to another language provided aligned Wordnets for two languages are available.

their work does not address the question of further improving the accuracy of WSD by using a small amount of training data from the target language.


The simplest strategy is to randomly annotate text from the target language and use it as training data.

this strategy of random sampling may not be the most optimum in terms of cost.


As per their model the best F-score achieved using manual cross-linking for ALL words was 73.34% for both Tourism and Health domain at a cost of 36K and 18K respectively.

using our model we obtain higher accuracies of 76.96% in the Tourism domain (using 1/3rd manual cross-links and 2K injection) at a lower total cost (32K rupees) and 75.57% in the Health domain (using only 1/3rd cross-linking and 1K injection) at a lower cost (16K rupees).


Many projected instances were filtered out by heuristics, and only 32.6% of the instances were left.

several instances were rescued by dictionary-based alignment correction and the number of projected instances increased from 31,652 to 39,891.


• The heuristic-based alignment filtering helps to improve the performance.

it is much worse than the baseline performance because of a falling-off in recall.


88% of the edges contain the correct semantic relations among the alternatives.

the baseline has pruned away 24% of the correct types and 26% of the correct semantic relations.


The previous section provided evidence that the document-to-document linking algorithm is capable of achieving high performance when parameters α, β are well selected.

section 5 indicated that it is more difficult to discover links across long document pairs.


Experiments on the ACE 2003 corpus showed that their method improved the overall performance by 2.8, 2.2 and 4.5 to 54.5, 64.0 and 60.8 in F1-measure on the NWIRE, NPAPER and BNEWS domains, respectively.

he did not look into the contribution of anaphoricity determi-nation on coreference resolution of different NP types.


2003) and improved the performance by 2.9 and 1.6 to 67.3 and 67.2 in F1-measure on the MUC-6 and MUC-7 corpora, respectively.

their experiments show that eliminating non-anaphors using an anaphoricity determination module in advance harms the performance.


Experiments on the ACE 2003 corpus showed that this joint anaphoricity-coreference ILP formulation improved the F1-measure by 3.7-5.3 on various domains.

their experiments assume true ACE mentions (i.e.


The phrase clustering algorithm in this paper outputs groups of source-language and targetlanguage phrases with similar meanings: paraphrases.

previous work on paraphrases for SMT has aimed at finding translations for source-language phrases in the system's input that weren't seen during system training.


Count-based metrics can deduce from the similar translations of two phrases that they have similar meanings, despite dissimilarity between the two word sequencese.g., they can deduce that "red" and "burgundy" belong in the same cluster.

these metrics are unreliable when total counts are low, since phrase co-occurrences are determined by a noisy alignment process.


Edit-based metrics are independent of how often phrases were observed.

sometimes they can be fooled by phrases that have similar word sequences but different meanings (e.g., "the dog bit the man" and "the man bit the dog", or "walk on the beach" and "don't walk on the beach").


4In our initial experiments, APL worked better than PL.


Only 2-4% of the total phrases in each language end up in a cluster (that's 6.5-9% of eligible phrases, i.e., of phrases that aren't "count 1").

about 20-25% of translation probabilities are smoothed for both language pairs.


There are several possibilities for future work based on new applications for phrase clusters: • In the experiments above, we used phrase clusters to smooth P(t|s) and P(s|t) when the pair (s,t) was observed in training data.

the phrase clusters often give non-zero probabilities for P(t|s) and P(s|t) when s and t were both in the training data, but didn't co-occur.


As Figure 5 shows for the variant LO cosine sentence, terms that are more frequent have a greater chance of being correctly translated at better ranks.

figure 5: Average rank of correct translation according to average source term frequency the relative performance of the different parametric configurations still holds.


The step for choosing similar document pairs in this work resembles some of our steps.

their work focuses on high quality and specific documents pairs, as opposed to the entire corpus of guaranteed quality we want to build.


We also notice that there is a relationship "conj" in syntactic dependency tree.

we find that it only connects two head words for a few coordinating conjunction, such as "and", "or", "but".


For rule based method, the seeds are selected in the review domain, which is more suitable for domain specific task.


MaxEnt classifier is a discrimitive model, which can incorporate various features.

it independently classifies each word, and ignores the dependency among successive words.


Table 3: Performance improvement (%) of including the additional features in an incremental way on the development data (of the abstracts subcorpus).

table 3 shows that the additional features behave quite differently in terms of PCLB and PCRB measures.


2) autoparse(test) consistently outperforms autoparse(t&t) on both the abstracts and the full papers subcorpora.

it is surprising to find that autoparse(t&t) achieves better performance on the clinical reports subcorpus than autoparse(test).


An effective semi-supervised extractor will have good performance over a range of extraction tasks and corpora.

many of the learning procedures just cited have been tested on only one or two extraction tasks, so their generality is uncertain.


S&G used WordNet 1 to provide word similarity information.

in the similarity-centric approach, lexical polysemy can lead the bootstrapping down false paths.


The document relevance score is first applied to rank the patterns in relevant documents, then the patterns with lexical similarity scores below a similarity threshold will be removed from the ranking; only patterns above threshold will be added to the seeds.

if in the current iteration, no pattern meets the threshold, the threshold will be lowered until new patterns can be found.


By then, "Earthquake" was trending on Twitter Search with thousands of updates 2 .

it is a daunting task for people to find out information they are interested in from such a huge number of news tweets, thus motivating us to conduct some kind of information extraction such as event mining, where SRL plays a crucial role (Surdeanu et al., 2003).


Given the simplicity of the baseline, the results obtained are quite high.

our approach F β=1 significantly improves baseline by 19% for recognition and 30% for classification.


On the one hand, it consists of a set of manually encoded rules based on morphosyntactic information.

it includes a Bayesian learned disambiguation module to identify nominal events.


It is especially interesting to integrate and summarize scattered opinions in blog articles and forums as they tend to represent the general opinions of a large number of people and get refreshed quickly as people dynamically generate new content, making them valuable for understanding the current views of a topic.

opinions in blogs and forums are usually fragmental, scattered around, and buried among other off-topic content, so it is quite challenging to organize them in a meaningful way.


Intuitively, the aspects should be concise phrases that can both be easily interpreted in the context of the topic under consideration and capture the major opinions.

where can we find such phrases and which phrases should we select as aspects?


these methods suffer from the problem of producing trivial aspects.


The closest work to ours are (Lu and Zhai, 2008;Sauper and Barzilay, 2009); both try to use well-written articles for summarization.

(Lu and Zhai, 2008) assumes the well-written article is structured with explicit or implicit aspect information, which does not always hold in practice, while (Sauper and Barzilay, 2009) needs a relatively large amount of training data in the given domain.


This indicates that people vary a lot in their preferences as which aspects should be presented first.

in cases when the random baseline outperforms others the margin is fairly small, while Freebase order and coherence-based order have a much larger margin of improvement when showing superior performance.


In our example, the morpheme-level intersection alignment is better as it has no misalignments and adds new alignments.


POMDPs, which make it possible to learn a policy that can maximize the averaged reward in partially observable environments (Pineau et al., 2003), have been successfully adopted in task-oriented dialogue systems for learning a dialogue control module from data (Williams and Young, 2007).

no work has attempted to use POMDPs for less (or non-) task-oriented dialogue systems, such as listening agents, because user goals are not as welldefined as task-oriented ones, complicating the finding of a reasonable reward function.


The perplexity of the human dialogues is less than that of the random system, but humans also exhibit a certain degree of freedom.

pOMDp's perplexity is less than the human dialogues; they still have some freedom, which probably led to their reasonable evaluation scores.


The parser outputs in SD and CoNLL can be assumed to be trees, so each node in the tree have only one parent node.

in the converted tree nodes can have more than one parent.


These results serve as a reference point for extrinsic evaluation results.

it should be  Table 2: Comparison of the F-score results with different SD variants on the development data set with the MC parser.


The lexicons that we use to perform lookups are collected by mining Wikipedia and other online resources (Mukund et al., 2010).

lexicon lookups will fail for Out-Of-Vocabulary words.


Light verbs are those which contribute to the tense and agreement of the verb (Butt and Geuder, 2001).

despite the existence of a light verb tag, it is noticed that in several sentences, verbs followed by auxiliary verbs need to be grouped as a single predicate.


In the case of judgment and appreciation, the use of the polarity reversal rule is straightforward ('POS jud' <=> 'NEG jud', 'POS app' <=> 'NEG app').

it is not trivial to find pairs of opposite emotions in the case of a fine-grained classification, except for 'joy' and 'sadness'.


Since long dependencies and those near to the root are typically the last constructed in transition-based parsing systems, it was concluded that MaltParser does suffer from some form of error propagation.

the richer feature representations of MaltParser led to improved performance in cases where error propagation has not occurred.


Since MSTParser and Malt-Parser produced Stanford dependencies for this experiment, evaluation required less manual examination than for some of the other parsers, as was also the case for the output of the Stanford parser in the original evaluation.

a manual evaluation was still performed in order to resolve questionable cases.


Genuine common instances are hyponymy relation candidates found in both S and U (G = X S ∩ X U ).

term pairs are obtained as virtual common instances when: • 1) they are extracted as hyponymy relation candidates in either S or U and; • 2) they do not seem to be a hyponymy relation in the other text The first condition corresponds to X S ⊕ X U .


Thus many virtual common instances would be a negative example for hyponymy relation acquisition.

genuine common instances (hyponymy relation candidates found in both S and U ) are more likely to hold a hyponymy relation than virtual common instances.


• B2 is the same as B1, except that both classifiers are trained with all available training data -WikiSet and WebSet are combined (27,500 training instances in total).

each classifier only uses its own feature set (WikiFeature or WebFeature) 5 .


Computing T (s) as an average of belief in its claims overestimates the trustworthiness of a source with relatively few claims; certainly a source with 90% accuracy over a hundred examples is more trustworthy than a source with 90% accuracy over ten.

summing the belief in claims allows a source with 10% accuracy to obtain a high trustworthiness score by simply making many claims.


Then, for each disjunctive clause consisting of a set P of positive literals (claims) and a set N of negations of literals, we add the constraint The left-hand side is the union bound of at least one of the claims being true (or false, in the case of negated literals); if this bound is at least 1, the constraint is satisfied.

this optimism can dilute the strength of our constraints by ignoring potential dependence among claims: x ⇒ y, x ∨ y implies y is true, but since we demand only when the claims are mutually exclusive, the union bound is exact; a common constraint is of the form q ⇒ r 1 ∨r 2 ∨.


Wikipedia Infoboxes (Wu and Weld, 2007) are a semi-structured source covering many domains with readily available authorship, and we produced our city population and basic biographic datasets from the most recent full-history dump of the English Wikipedia (taken January 2008).

attribution is difficult: if an author edits the page but not the claim within the infobox, is the author implicitly agreeing with (and asserting) the claim?


More importantly, Figure 1 also indicates that this method shows more stable results and low variation in summary quality when keyphrases of size 3 or smaller are employed.

mmR shows high variation in summary qualities making summaries that obtain pyramid scores as low as 0.15.


A promising way of overcoming this weakness is to include n-grams, generalizing the bag-ofwords model into a bag-of-phrases model (Baccianella et al., 2009;Pang and Lee, 2008).

regression models over the feature space of all n-grams (for either fixed maximal n or variable-length phrases) are computationally expensive in their training phase.


As shown in table 2, when we only use Word Feature(WF), the F-value of task (a) achieved a high value (96.3).

the F-values of task (b) and (c) are relative low, that means the problem of recognizing the eight basic emotions for emotion words is a lot more difficult than the problem of recognizing emotion and unemotion words, so we focus on task (b) and (c).


", it would be helpful for a QA system to know which NEs are cities.

virtually all of the existing NE recognizers and mention detectors can only determine whether an NE is a location or not.


To do so, we create a pairwise factor node that connects two variable nodes if the aforementioned relation between the underlying NPs is satisfied.

to implement this idea, we need to address two questions.


Ravi and Knight (2009) mention that it is possible to interrupt the IP solver and obtain a suboptimal solution faster.

the IP solver did not return any solution when provided the same amount of time as taken by MIN-GREEDY for any of the data settings.


The figure shows that the greedy approach can scale comfortably to large data sizes, and a complete run on the entire Penn Treebank data finishes in just 1485 seconds.

the IP method does not scale well-on average, it takes 93 seconds to finish on the 24k test (versus 34 seconds for MIN-GREEDY) and on the larger PTB test data, the IP solver runs for  It is interesting to see that for the 24k dataset, the greedy strategy finds a grammar set (containing only 478 tag bigrams).


The intuition behind "more informative" is that these instances support the learning process, so we might need fewer annotated instances to achieve a comparable classifier performance, which could decrease the cost of annotation.

"more informative" also means that these instances might be more difficult to annotate, so it is only fair to assume that they might need more time for annotation, which increases annotation cost.


Overall, the sentences selected by the classifier during AL are longer (26.2 vs. 28.1 token per sentence), and thus may take the annotators more time to read.

3 we could not find a significant correlation (Spearman rank correlation test) between sentence length and annotation time, nor between sentence length and classifier confidence.


Various aspects of route directions have been subject of research in computational linguistics, ranging from instructional dialogues in MapTask (Anderson et al., 1991) to recent work on learning to follow route directions (Vogel and Jurafsky, 2010).

little work has been done on generating NL directions based on data from Geographical Information Systems (Dale et al., 2005;Roth and Frank, 2009).


In addition, (semi-)supervised models could be used to assess the gain we may achieve in comparison to the minimally supervised setting.

we still see potential for improving our current models by integrating refinements based on the observations outlined above: Missing alignment targets on the linguistic side -especially due to anaphora, elliptical or aggregating constructions -constitute the main error source.


That was because the number of resulting clus-ters should be known as a parameter in the latter.

the number of corpus domains might be unknown in our case.


In comparison to Turkish, isiZulu is a tonal language.

to East Asian languages, in isiZulu there are three steps for tone assignment: lexical, morphemic and terraced.


Similar to Harris (Harris, 1955), the algorithm is based on letter frequencies.

when Harris uses successor and predecessor frequencies, they use position-independent n-gram statistics to merge single letters into morphemes until a stopping criterion is fulfilled.


Therefore the summarization problem can be formulated as the minimum dominating set problem.

usually there is a length restriction for generating the summary.


Progress has been made and promising results have been reported in the past years for both DS and PB approaches.

most previous research work (some exceptions are discussed in related work) involves solely one category of approach.


The consensus is that the lexical items exposing similar behavior in a large body of text most likely have the same meaning.

the concepts of marriage and political regime, that are also observed in similar lexico-syntactic environments, albeit having quite distinct meanings are likewise assigned by such methods to the same cluster.


Since there is no large metaphor-annotated corpus available, it was impossible for us to reliably evaluate the recall of the system.

the system identified a total number of 4456 metaphorical expressions in the BNC starting with a seed set of only 62, which is a promising result.


Thanks to its simplicity, services with social tagging features have attracted a lot of users and have accumulated huge amount of annotations.

comparing to taxonomies, social tagging has an inherent shortcoming, that there is no explicit hierarchical relations between tags.


One of TAG-TAG's benefits is that it does not rely on the content of the annotated document, thus it can be applied to tags for non-text objects, such as images and music.

when coming to text documents, this benefit is also a shortcoming, that TAG-TAG makes no use of the content when it is available.


A person searching for forms containing the potential aspect would have to search for 'nga<asp> + ng<asp>'.

there should be no ambiguity, as the orthographic form would eliminate this.


On the one hand, the restrictions thus imposed by bipartite matching penalize sets of proposed analyses that do not differentiate between surface-identical syncretic morphemes.

the same one-to-one matching restrictions penalize proposed analyses that do not conflate allomorphs of the same underlying morpheme, whether those allomorphs are phonologi-cally induced or not.


For example, the LTI index, computed over automatically extracted local topics, produces Topic Control assignments with the average precision of 80% when compared to assignments derived from human-annotated data using the strict accuracy metric.

automated prediction of Involvement based on NPI index is far less reliable, although we can still pick the most involved speaker with 67% accuracy.


Recent research tries to automatically align the bilingual syntactic sub-trees.

most of these works suffer from the following problems.


There are cases that the correctly-aligned tree pairs have very few links, while we have a bunch of candidates with lower alignment probabilities.

the sum of the lower probabilities is larger than that of the correct links', since the number of correct links is much fewer.


On one hand, GIZA++ is offline trained on a large amount of bilingual sentences to compute the lexical and word alignment features.

the tree structural features, similar to word and phrase penalty features in phrase based SMT models, are computed online for both training and testing.


Also, SCFs perform considerably better than COs in the English experiment (we only have the result for F4 available, but it is considerably lower than the result for F3).

earlier English studies have reported contradictory results (e.g.


When considering the general level of performance, our best performance for French (65.4 F) is lower than the best performance for English in the experiment of Sun and Korhonen (2009).

it does compare favourably to the performance of other stateof-the-art (even supervised) English systems (Joanis et al., 2008;Li and Brew, 2008;Ó Séaghdha and Copestake, 2008;Vlachos et al., 2009).


Our results contrast with those of Ferrer who showed that a clustering approach does not transfer well from English to Spanish.

she used basic SCF and named entity features only, and a clustering algorithm less suitable for high dimensional data.


A dependency forest has a structure of a hypergraph such as packed forest (Klein and Manning, 2001;Huang and Chiang, 2005).

while each hyperedge in a packed forest naturally treats the corresponding PCFG rule probability as its weight, it is challenging to make dependency forest to be a weighted hypergraph because dependency parsers usually only output a score, which can be either positive or negative, for each edge in a dependency tree rather than a hyperedge in a  dependency forest.


Input: a source sentence ψ, a forest F , an alignment a, and k Output: minimal initial phrase set R 1: for each node v ∈ V in a bottom-up order do 2: for each hyperedge e ∈ E and head(e) = v do 3: W ← ∅ 4: fixs ← EnumFixed(v, modif iers(e)) 5: floatings ← EnumFloating(modif iers(e)) 6: add structures fixs, floatings to W 7: for each ω ∈ W do 8: if ω is consistent with a then 9: generate a rule r 10: R.append(r) 11: keep k-best dependency structures for v In tree-based rule extraction, one just needs to first enumerate all bilingual phrases that are consistent with word alignment and then check whether the dependency structures over the target phrases are well-formed.

this algorithm fails to work in the forest scenario because there are usually exponentially many well-formed structures over a target phrase.


(2008) show that string-to-dependency system achieves 1.48 point improvement in BLEU along with dependency language model, while no improvement without it.

the string-todependency system still commits to using dependency language model from noisy 1-best trees.


The Prague Dependency Treebank also contains annotation for light verb constructions (Cinková and Kolářová, 2005) and NomBank (Meyers et al., 2004b) provides the argument structure of common nouns, paying attention to those occurring in support verb constructions as well.

zarrieß and Kuhn (2009) make use of translational correspondences when identifying multiword expressions (among them, light verb constructions).


The recognition of light verb constructions cannot be solely based on syntactic patterns for other (productive or idiomatic) combinations may exhibit the same verb + noun scheme (see section 2).

in agglutinative languages such as Hungarian, nouns can have several grammatical cases, some of which typically occur in a light verb construction when paired with a certain verb.


For the above reasons, a single light verb construction manifests in several different forms in the corpus.

each occurrence is manually paired with its prototypical (i.e.


occurs 5.8 times in the corpus on average.

the participle form irányadó occurs in 607 instances (e.g.


In this case, the computer must first recognize that the parts of the collocation form one unit (Oravecz et al., 2004), for which the multiword context of the given word must be considered.

the lack (or lower degree) of compositionality blocks the possibility of word-by-word translation (Siepmann, 2005;Siepmann, 2006).


Clearly, for the time being this process cannot be done by a computer at the level of a human expert.

important tasks may be automated such as market forecasting, which relies on identifying and aggregating relevant information from the World Wide Web (Berekoven et.


Under a market we unite branches, products, and technologies, because the distinction between these is not clear in general (e.g., for semiconductors).

we define a criterion to be a metric attribute that can be measured over time.


decreasing or increasing) cannot be derived from the given values.

this information can mostly be obtained from a nearby indicator word (e.g.


W C : This affinity matrix aims to reflect the cross-document relationships between sentences in the document set.

the relationships in this matrix are used for carrying the influences of the sentences in other documents on the local saliency of the sentences in a particular document.


Overall, the proposed unified graph-based approach is effective for both single document summarization and multi-document summarization.

the performance improvement for singledocument summarization is more significant than that for multi-document summarization, which shows that the global information in a document set is very beneficial to summarization of each single document in the document set.


A distinct advantage of rulebased bots is that they have very high precision.

they suffer from fixed-size knowledge bases and use only rigid rules.


are highly reliable to be real questions.

the initial training data still contain many sentences ending with "?"


Work on answer patterns includes the web based pattern mining (Zhang and Lee, 2002;Du et al., 2005) and a combination of syntactic and semantic elements (Soubbotin and Soubbotin, 2002) etc.

to previous work, we do not only focus on standard language corpus, but extensively explore characteristics of online questions.


Since the vocabulary size of possible character-tag-pairs is limited, the characterbased models can tolerate out-of-vocabulary (OOV) words and have become the dominant technique for CWS in recent years.

statistical approaches can also be classified as either adopting a generative model or adopting a discriminative model.


Having analyzed the scores of the model scanning from both directions, we found that the original scores (from left-to-right scan) at the stages "者" and "宿" indeed get better if the model scans from right-to-left.

the score at the stage "露" deteriorates because the useful feature "者" (a past non-adjacent character for "露" when scans form right-toleft) still cannot be utilized when the past context "宿者" as a whole is unseen, when the related probabilities are estimated via modified Kneser-Ney smoothing (Chen and Goodman, 1998) technique.


Two scanning modes seem not complementing each other, which is out of our original expectation.

we found that the character-based generative model and the discriminative one complement each other much more than the two scanning modes do.


Generally speaking, if the "G(or D)+" has a strong preference on the desired candidate, but the "D(or G)-" has a weak preference on its top-1 incorrect candidate, then this combining method would correct most "G+D-(also G-D+)" errors.

the advantage of combining two models would vanish if the "G(or D)+" has a weak preference while the "D(or G)-" has a strong preference over their top-1 candidates.


From the results, it can be seen that the generative model achieves comparable results with the discriminative one and they outperform each other on different corpus.

the generative model exceeds the discriminative one on R IV (0.973 vs. 0.956) but loses on R OOV (0.511 vs. 0.680).


Last, it is found that weighting various features differently would give better result.

further study is required to find out the true reason for this strange but interesting ph The authors extend sincere thanks to Wenbing Jiang for his helps with our experiments.


They adopt a holistic lexicon-based approach to solve this problem, which exploits external information and evidences in other sentences and other reviews.

in this paper, we obtain the prior knowledge of a product by mining the web, and then use such knowledge to determine the SO of DSAAs.


(2006) is similar to ours because they also define the sentiment score of a word by its composite characters.

their algorithm is based only on frequency, while we exploit point mutual information that can capture the character-sentiment association.


For a positive expectation noun, people usually expect the thing referred to by the noun to be bigger, higher or happen frequently.

for a negative expectation noun, people usually expect the thing referred to by the noun to be smaller, lower or don' t happen .


Obviously, we can construct the potential tokenizations and translations by only using the extracted rules, in line with traditional translation decoding.

it may limits the potential tokenization space.


3 Using all rule table, our joint method significantly outperforms the best single system SF by +1.96 and +1.66 points on MT04 and MT05 respectively, and also outperforms the lattice-based system by +1.46 and +0.93 points.

the 8 tokenization features have small impact on the lattice system, probably because the tokenization space limited  Table 3: Effect of tokenization features on Chinese-English translation task. "


With high-quality emotion lexicons, systems using simple methods can achieve competitive performance.

to manually build an emotion lexicon is timeconsuming.


The above section introduced a method to rank words with a few seed emotion words.

to build emotion lexicons requires that we manually remove the noises incurred by the automatic ranking method.


In fact, most of words in the emotion nouns extracted can be used as verbs or adjectives in Chinese.

since CCD is not designed for emotion analysis, words which are expression of emotions such as 哭泣 (cry) or evaluation such as 胆小 (cowardice) were included.


The side-effect of introducing false positives for AM-1 is to lower accuracy.

the AM-1 model outperforms both AM-2 and BL-2 models on F-measure ( Figure 6), with an average increase of 5.2 and 3.4 percentage points respectively.


Recently, contextual source-language features have been incorporated into translation models to predict translation phrases for traveling domain tasks (Stroppa et al., 2007;Haque et al., 2009).

we are not aware of any work addressing contextual modeling for statistical translation of spoken meeting-style interactions, not least due to the lack of a relevant corpus.


System 6 shows a more even performance across dev and eval sets than our trained system, which may reflect some degree of overtuning of our systems to the relatively small development set (about 7K words).

the PER scores of System 6 are significantly worse compared to our in-house systems.


After extracting the phrasal (rule) tables for each data source, they were combined into a single phrasal (rule) table using the same combination approach as for the basic phrase-based system.

the translation results (BLEU/PER of 24.0/46.6 (dev) and 20.8/47.6 (eval), respectively) did not show any improvement over the basic phrasebased system.


Our goal was not to determine how translation of meeting style data can be improved in general -better translations could certainly be generated by better syntactic modeling, addressing morphological variation in German, and generally improving phrasal coverage, in particular for sentences involving colloquial expressions.

these are fairly general problems of SMT that have been studied previously.


Because the training takes polynomial time in the number of common features in x (|x at each round, we need to set N to a smaller value when we take higher-order conjunctive features into consideration.

since the margin computation takes linear time in the number of support vectors |S R | relevant to rare features f R ∈ F \F C , we need to set N to a larger value when we handle a larger number of training examples.


Near-synonyms are useful knowledge resources for many natural language applications such as query expansion for information retrieval (IR) and paraphrasing for text generation.

near-synonyms are not necessarily interchangeable in contexts due to their specific usage and syntactic constraints.


In this paper, we consider the near-synonym substitution task as a classification task, where a classifier is trained for each near-synonym set to classify test examples into one of the nearsynonyms in the set.

near-synonyms share more common context words (features) than semantically dissimilar words in nature.


Ideally, the correct answers should be provided by human experts.

such data is usually unavailable, especially for a large set of test examples.


Table 1 shows some examples, where Example 3 is an example of misclassification.

although Example 2 is a correct classification, it might be an ambiguous case to classifiers since the scores are close among classes.


A zero weight indicates that the dimension word does not occur in the input sentence, thus the corresponding dimension of each column vector will not be adjusted.

the corresponding dimension of the column vector of the correct class ( j k = ) is adjusted by adding a value, while those of the competing classes ( j k ≠ ) are adjusted by subtracting a value from them.


Let the general distributional meaning of the word w be w. Their model computes a different vector w s that represents the specific distributional meaning of w with respect to s, i.e.

: In general, this operator gives different vectors for each word w i in the sequence The model of Erk and Padó (2008) was designed to disambiguate the distributional meaning of a word w in the context of the sequence s. substituting the word w with the semantic head h of s, allows to compute the distributional meaning of sequence s as shaped by the word that is governing the sequence (c.f.


To suit our need, we tested the k-means clustering with distributional similarity.

it does not perform as well as the proposed method.


In the first iteration of EM, soft-labeled examples SL are treated in the same way as the labeled examples in L. Thus both SL and L are used as labeled examples to learn the initial classifier f 0 .

in the subsequent iterations, SL is treated in the same way as any examples in U.